增强的异质图注意网络,具有用于文档级关系提取的新型多标签焦点损失
1State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China.
Entropy (Basel, Switzerland)
|March 28, 2024
概括
本研究引入了提及级框架来提取文档级关系,通过专注于特定实体提及而不是抽象概念来提高准确性. 增强的图表注意网络有效地模拟了长距离的语义关系,以更好地预测关系.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 提取信息 提取信息
背景情况:
- 文档级关系提取旨在识别文档中的实体之间的所有关系.
- 现有的方法经常使用整体实体表示,可能会从细粒度的提及中丢失信息.
- 建议在特定实体实例中转向提及级分析,以预测特定实体实例中的地面关系.
研究的目的:
- 提出一个新的两阶段,提及级框架,用于文档级关系提取.
- 通过使用细粒度的实体提及来增强句内和句间关系的建模.
- 通过考虑远距离的语义依赖关系和共同引用信息来改善关系推断.
主要方法:
- 一个两阶段的框架,利用本地和全球提及表示.
- 一个增强的异质图的注意力网络,用于建模句间关系.
- 一个基于实体-核心引用路径的关系推理策略.
- 一个新的基于交叉的多标记焦点损失函数来处理类不平衡和多标签预测.
主要成果:
- 拟议的提及级框架在文档级关系提取方面明显优于现有方法.
- 增强的异质图表注意力网络有效地捕捉了长距离的语义关系.
- 新的损失函数成功地解决了阶级不平衡和多标签预测挑战.
结论:
- 在特定的实体提及中预测接地关系对于有效的文档级关系提取至关重要.
- 拟议的框架在准确识别多个句子之间的关系方面取得了重大进展.
- 未来的工作可以在更复杂的信息提取任务中建立在此提及层次的方法之上.
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